TY - GEN
T1 - System identification of Just Walk
T2 - 2017 American Control Conference, ACC 2017
AU - Freigoun, Mohammad T.
AU - Martin, Cesar A.
AU - Magann, Alicia B.
AU - Rivera, Daniel
AU - Phatak, Sayali S.
AU - Korinek, Elizabeth V.
AU - Hekler, Eric B.
N1 - Publisher Copyright:
© 2017 American Automatic Control Council (AACC).
PY - 2017/6/29
Y1 - 2017/6/29
N2 - There is significant evidence to show that physical activity reduces the risk of many chronic diseases. With the rise of mobile health (mHealth) technologies, one promising approach is to design interventions that are responsive to an individual's changing needs. This is the overarching goal of Just Walk, an intensively adaptive physical activity intervention that has been designed on the basis of system identification and control engineering principles. Features of this intervention include the use of multisine signals as pseudo-random inputs for providing daily step goals and reward targets for participants, and an unconventional ARX estimation-validation procedure applied to judiciously-selected data segments that seeks to balance predictive ability over validation data segments with overall goodness of fit. Analysis of the estimated models provides important clues to individual participant characteristics that influence physical activity. The insights gained from black-box modeling are critical to building semi-physical models based on a dynamic extension of Social Cognitive Theory.
AB - There is significant evidence to show that physical activity reduces the risk of many chronic diseases. With the rise of mobile health (mHealth) technologies, one promising approach is to design interventions that are responsive to an individual's changing needs. This is the overarching goal of Just Walk, an intensively adaptive physical activity intervention that has been designed on the basis of system identification and control engineering principles. Features of this intervention include the use of multisine signals as pseudo-random inputs for providing daily step goals and reward targets for participants, and an unconventional ARX estimation-validation procedure applied to judiciously-selected data segments that seeks to balance predictive ability over validation data segments with overall goodness of fit. Analysis of the estimated models provides important clues to individual participant characteristics that influence physical activity. The insights gained from black-box modeling are critical to building semi-physical models based on a dynamic extension of Social Cognitive Theory.
UR - http://www.scopus.com/inward/record.url?scp=85027055144&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027055144&partnerID=8YFLogxK
U2 - 10.23919/ACC.2017.7962940
DO - 10.23919/ACC.2017.7962940
M3 - Conference contribution
AN - SCOPUS:85027055144
T3 - Proceedings of the American Control Conference
SP - 116
EP - 121
BT - 2017 American Control Conference, ACC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 May 2017 through 26 May 2017
ER -